UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning

Abstract

A remote sensing technique was developed to detect citrus canker in laboratory conditions and was verified in the grove by utilizing an unmanned aerial vehicle (UAV). In the laboratory, a hyperspectral (400–1000 nm) imaging system was utilized for the detection of citrus canker in several disease development stages (i.e., asymptomatic, early, and late symptoms) on Sugar Belle leaves and immature (green) fruit by using two classification methods: (i) radial basis function (RBF) and (ii) K nearest neighbor (KNN). The same imaging system mounted on an UAV was used to detect citrus canker on tree canopies in the orchard. The overall classification accuracy of the RBF was higher (94%, 96%, and 100%) than the KNN method (94%, 95%, and 96%) for detecting canker in leaves. Among the 31 studied vegetation indices, the water index (WI) and the Modified Chlorophyll Absorption in Reflectance Index (ARI and TCARI 1) more accurately detected canker in laboratory and in orchard conditions, respectively. Immature fruit was not a reliable tissue for early detection of canker. However, the proposed technique successfully distinguished the late stage canker-infected fruit with 92% classification accuracy. The UAV-based technique achieved 100% classification accuracy for identifying healthy and canker-infected trees.

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

“…Machine learning algorithms have the advantage of modeling data in a non-linear and a non-parametric manner. Unlike many traditional statistical methods, these algorithms are built with the advantage of dealing with noisy, complex, and heterogeneous data [16,23,[50][51][52]. These characteristics proved to be an advantage for this study, as the data used had higher variance, was not-normal (Table 3), and, while statistically significant, low-correlated in a pairwise manner (Figure 4).…”

“…Remote sensing techniques can be useful for the estimation of plant health conditions, including monitoring the nutritional status [1][2][3][4], the stress response [5][6][7], plant count [8,9], yield prediction [10][11][12], chlorophyll content [13][14][15], pest and disease identification [16,17], and biomass estimation [18], among others. Multisensory data is often used to accomplish this task, including the ones acquired by orbital sensors, aircraft or Unnamed Aerial Vehicle (UAV)-embedded cameras, terrestrial sensors, and field spectroradiometers, known as proximal sensors [19][20][21][22][23].…”

“…They are also capable to perform numerous combinations and calculations in a matter of seconds, achieving relative success in remote sensing applications regarding plant analysis [48,49]. Concerning hyperspectral measurements, among the applications evaluated, these algorithms were able to return state-of-the-art performances for many situations [5,16,23,[50][51][52]. Even though, to date, no study evaluated the performance of machine learning algorithms in inferring both plant macro-and micro-nutritional content with only leaf hyperspectral measurements.…”

UAV hyperspectral inversion modeling of rice nitrogen content based on WOA-ELM

“…Among them, the vegetation index method had clear physical meaning and simple model construction. But at present, there are a large number of spectral indexes constructed, which means a single vegetation index cannot well represent the whole hyperspectral information [18] ..…”

Evaluation of a Ground Penetrating Radar to Map the Root Architecture of HLB-Infected Citrus Trees

“…Sandy soil (typical of southwest Florida citrus groves) has a rapid and high water infiltration rate, which may affect GPR performance. • Artificial intelligence and machine learning have been utilized to correctly identify and classify objects, such as crops [44], crop pests [45][46][47], and diseases [48][49][50][51][52]. A similar approach could be adopted to automate the root detection procedure by analyzing and identify "root" hyperbolas that are produced by GPR, by utilizing artificial intelligence and machine learning.…”